Highway Traffic Flow Prediction Supporting Multi-Traffic Parameter Detection

Freeway traffic flow prediction is of great significance to freeway traffic management, route planning, toll strategy development and public safety. Existing traffic flow prediction methods mainly use deep learning models, which mainly have the following two problems: on the one hand, the special characteristics of the highway network structure make the common urban network traffic flow prediction methods inapplicable; on the other hand, the influence of external factors, such as weather and accidents, on the flow is ignored. Aiming at the above problems, this paper proposes a highway traffic flow prediction algorithm that supports multi-traffic parameter detection. Firstly, the topological map conforming to the highway network structure is constructed, which makes the traffic distribution conform to the reality; secondly, the initial prediction of the traffic is made by using the Informer time series model; finally, the traffic adjustment template is constructed by using the regression model according to the multi-traffic parameters. In this paper, multiple angles are selected to test the performance of the algorithm on two real datasets, and the results show that the performance of the algorithm in this paper is significantly improved compared with other mainstream traffic models.